Overview

Dataset statistics

Number of variables14
Number of observations6019
Missing cells5311
Missing cells (%)6.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory658.5 KiB
Average record size in memory112.0 B

Variable types

Numeric5
Text5
Categorical4

Alerts

Year is highly overall correlated with Kilometers_DrivenHigh correlation
Kilometers_Driven is highly overall correlated with YearHigh correlation
Price is highly overall correlated with TransmissionHigh correlation
Transmission is highly overall correlated with PriceHigh correlation
Fuel_Type is highly imbalanced (53.4%)Imbalance
Owner_Type is highly imbalanced (60.9%)Imbalance
New_Price has 5195 (86.3%) missing valuesMissing
Kilometers_Driven is highly skewed (γ1 = 58.72466189)Skewed
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique

Reproduction

Analysis started2025-09-26 12:31:02.424409
Analysis finished2025-09-26 12:31:07.526963
Duration5.1 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct6019
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3009
Minimum0
Maximum6018
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size47.2 KiB
2025-09-26T12:31:07.746970image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile300.9
Q11504.5
median3009
Q34513.5
95-th percentile5717.1
Maximum6018
Range6018
Interquartile range (IQR)3009

Descriptive statistics

Standard deviation1737.68
Coefficient of variation (CV)0.57749417
Kurtosis-1.2
Mean3009
Median Absolute Deviation (MAD)1505
Skewness0
Sum18111171
Variance3019531.7
MonotonicityStrictly increasing
2025-09-26T12:31:07.892381image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
4009 1
 
< 0.1%
4018 1
 
< 0.1%
4017 1
 
< 0.1%
4016 1
 
< 0.1%
4015 1
 
< 0.1%
4014 1
 
< 0.1%
4013 1
 
< 0.1%
4012 1
 
< 0.1%
4011 1
 
< 0.1%
Other values (6009) 6009
99.8%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
6018 1
< 0.1%
6017 1
< 0.1%
6016 1
< 0.1%
6015 1
< 0.1%
6014 1
< 0.1%
6013 1
< 0.1%
6012 1
< 0.1%
6011 1
< 0.1%
6010 1
< 0.1%
6009 1
< 0.1%

Name
Text

Distinct1876
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
2025-09-26T12:31:08.221387image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length59
Median length48
Mean length26.161987
Min length11

Characters and Unicode

Total characters157469
Distinct characters69
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique842 ?
Unique (%)14.0%

Sample

1st rowMaruti Wagon R LXI CNG
2nd rowHyundai Creta 1.6 CRDi SX Option
3rd rowHonda Jazz V
4th rowMaruti Ertiga VDI
5th rowAudi A4 New 2.0 TDI Multitronic
ValueCountFrequency (%)
maruti 1211
 
4.2%
hyundai 1107
 
3.8%
honda 608
 
2.1%
at 548
 
1.9%
diesel 506
 
1.7%
1.2 419
 
1.4%
toyota 411
 
1.4%
tdi 392
 
1.3%
swift 353
 
1.2%
mt 339
 
1.2%
Other values (862) 23226
79.8%
2025-09-26T12:31:08.766756image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
23101
 
14.7%
a 9788
 
6.2%
i 9460
 
6.0%
e 7731
 
4.9%
t 6582
 
4.2%
o 6572
 
4.2%
n 6501
 
4.1%
r 6372
 
4.0%
u 4347
 
2.8%
d 4099
 
2.6%
Other values (59) 72916
46.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 80821
51.3%
Uppercase Letter 37012
23.5%
Space Separator 23101
 
14.7%
Decimal Number 12882
 
8.2%
Other Punctuation 2228
 
1.4%
Dash Punctuation 1149
 
0.7%
Close Punctuation 138
 
0.1%
Open Punctuation 138
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 9788
12.1%
i 9460
11.7%
e 7731
9.6%
t 6582
8.1%
o 6572
8.1%
n 6501
8.0%
r 6372
7.9%
u 4347
 
5.4%
d 4099
 
5.1%
l 3464
 
4.3%
Other values (16) 15905
19.7%
Uppercase Letter
ValueCountFrequency (%)
S 3293
 
8.9%
T 3225
 
8.7%
M 3030
 
8.2%
D 2939
 
7.9%
V 2668
 
7.2%
C 2556
 
6.9%
I 2532
 
6.8%
X 2079
 
5.6%
H 2000
 
5.4%
A 1988
 
5.4%
Other values (16) 10702
28.9%
Decimal Number
ValueCountFrequency (%)
0 3046
23.6%
2 2852
22.1%
1 2841
22.1%
5 1184
 
9.2%
4 799
 
6.2%
3 761
 
5.9%
8 490
 
3.8%
6 472
 
3.7%
7 298
 
2.3%
9 139
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 2210
99.2%
/ 16
 
0.7%
& 2
 
0.1%
Space Separator
ValueCountFrequency (%)
23101
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1149
100.0%
Close Punctuation
ValueCountFrequency (%)
) 138
100.0%
Open Punctuation
ValueCountFrequency (%)
( 138
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 117833
74.8%
Common 39636
 
25.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 9788
 
8.3%
i 9460
 
8.0%
e 7731
 
6.6%
t 6582
 
5.6%
o 6572
 
5.6%
n 6501
 
5.5%
r 6372
 
5.4%
u 4347
 
3.7%
d 4099
 
3.5%
l 3464
 
2.9%
Other values (42) 52917
44.9%
Common
ValueCountFrequency (%)
23101
58.3%
0 3046
 
7.7%
2 2852
 
7.2%
1 2841
 
7.2%
. 2210
 
5.6%
5 1184
 
3.0%
- 1149
 
2.9%
4 799
 
2.0%
3 761
 
1.9%
8 490
 
1.2%
Other values (7) 1203
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 157469
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
23101
 
14.7%
a 9788
 
6.2%
i 9460
 
6.0%
e 7731
 
4.9%
t 6582
 
4.2%
o 6572
 
4.2%
n 6501
 
4.1%
r 6372
 
4.0%
u 4347
 
2.8%
d 4099
 
2.6%
Other values (59) 72916
46.3%

Location
Categorical

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
Mumbai
790 
Hyderabad
742 
Kochi
651 
Coimbatore
636 
Pune
622 
Other values (6)
2578 

Length

Max length10
Median length7
Mean length6.8466523
Min length4

Characters and Unicode

Total characters41210
Distinct characters27
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMumbai
2nd rowPune
3rd rowChennai
4th rowChennai
5th rowCoimbatore

Common Values

ValueCountFrequency (%)
Mumbai 790
13.1%
Hyderabad 742
12.3%
Kochi 651
10.8%
Coimbatore 636
10.6%
Pune 622
10.3%
Delhi 554
9.2%
Kolkata 535
8.9%
Chennai 494
8.2%
Jaipur 413
6.9%
Bangalore 358
5.9%

Length

2025-09-26T12:31:08.979872image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mumbai 790
13.1%
hyderabad 742
12.3%
kochi 651
10.8%
coimbatore 636
10.6%
pune 622
10.3%
delhi 554
9.2%
kolkata 535
8.9%
chennai 494
8.2%
jaipur 413
6.9%
bangalore 358
5.9%

Most occurring characters

ValueCountFrequency (%)
a 6051
14.7%
e 3630
 
8.8%
i 3538
 
8.6%
o 2816
 
6.8%
b 2392
 
5.8%
r 2149
 
5.2%
n 1968
 
4.8%
d 1932
 
4.7%
h 1923
 
4.7%
u 1825
 
4.4%
Other values (17) 12986
31.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35191
85.4%
Uppercase Letter 6019
 
14.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6051
17.2%
e 3630
10.3%
i 3538
10.1%
o 2816
8.0%
b 2392
 
6.8%
r 2149
 
6.1%
n 1968
 
5.6%
d 1932
 
5.5%
h 1923
 
5.5%
u 1825
 
5.2%
Other values (8) 6967
19.8%
Uppercase Letter
ValueCountFrequency (%)
K 1186
19.7%
C 1130
18.8%
M 790
13.1%
H 742
12.3%
P 622
10.3%
D 554
9.2%
J 413
 
6.9%
B 358
 
5.9%
A 224
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 41210
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6051
14.7%
e 3630
 
8.8%
i 3538
 
8.6%
o 2816
 
6.8%
b 2392
 
5.8%
r 2149
 
5.2%
n 1968
 
4.8%
d 1932
 
4.7%
h 1923
 
4.7%
u 1825
 
4.4%
Other values (17) 12986
31.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6051
14.7%
e 3630
 
8.8%
i 3538
 
8.6%
o 2816
 
6.8%
b 2392
 
5.8%
r 2149
 
5.2%
n 1968
 
4.8%
d 1932
 
4.7%
h 1923
 
4.7%
u 1825
 
4.4%
Other values (17) 12986
31.5%

Year
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.3582
Minimum1998
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.2 KiB
2025-09-26T12:31:09.104929image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1998
5-th percentile2007
Q12011
median2014
Q32016
95-th percentile2018
Maximum2019
Range21
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2697421
Coefficient of variation (CV)0.001624024
Kurtosis0.89420088
Mean2013.3582
Median Absolute Deviation (MAD)2
Skewness-0.84580214
Sum12118403
Variance10.691214
MonotonicityNot monotonic
2025-09-26T12:31:09.219549image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2014 797
13.2%
2015 744
12.4%
2016 741
12.3%
2013 649
10.8%
2017 587
9.8%
2012 580
9.6%
2011 466
7.7%
2010 342
5.7%
2018 298
 
5.0%
2009 198
 
3.3%
Other values (12) 617
10.3%
ValueCountFrequency (%)
1998 4
 
0.1%
1999 2
 
< 0.1%
2000 4
 
0.1%
2001 8
 
0.1%
2002 15
 
0.2%
2003 17
 
0.3%
2004 31
 
0.5%
2005 57
0.9%
2006 78
1.3%
2007 125
2.1%
ValueCountFrequency (%)
2019 102
 
1.7%
2018 298
 
5.0%
2017 587
9.8%
2016 741
12.3%
2015 744
12.4%
2014 797
13.2%
2013 649
10.8%
2012 580
9.6%
2011 466
7.7%
2010 342
5.7%

Kilometers_Driven
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3093
Distinct (%)51.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58738.38
Minimum171
Maximum6500000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.2 KiB
2025-09-26T12:31:09.372292image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum171
5-th percentile13007.4
Q134000
median53000
Q373000
95-th percentile120000
Maximum6500000
Range6499829
Interquartile range (IQR)39000

Descriptive statistics

Standard deviation91268.843
Coefficient of variation (CV)1.5538195
Kurtosis4125.0941
Mean58738.38
Median Absolute Deviation (MAD)19483
Skewness58.724662
Sum3.5354631 × 108
Variance8.3300017 × 109
MonotonicityNot monotonic
2025-09-26T12:31:09.555737image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60000 82
 
1.4%
45000 70
 
1.2%
65000 68
 
1.1%
50000 61
 
1.0%
55000 60
 
1.0%
70000 60
 
1.0%
30000 54
 
0.9%
52000 54
 
0.9%
80000 50
 
0.8%
75000 50
 
0.8%
Other values (3083) 5410
89.9%
ValueCountFrequency (%)
171 1
 
< 0.1%
600 1
 
< 0.1%
1000 9
0.1%
1001 2
 
< 0.1%
1011 1
 
< 0.1%
1048 1
 
< 0.1%
1261 1
 
< 0.1%
1331 1
 
< 0.1%
1400 1
 
< 0.1%
1617 1
 
< 0.1%
ValueCountFrequency (%)
6500000 1
< 0.1%
775000 1
< 0.1%
720000 1
< 0.1%
620000 1
< 0.1%
480000 2
< 0.1%
445000 1
< 0.1%
300000 1
< 0.1%
299322 1
< 0.1%
282000 1
< 0.1%
262000 1
< 0.1%

Fuel_Type
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
Diesel
3205 
Petrol
2746 
CNG
 
56
LPG
 
10
Electric
 
2

Length

Max length8
Median length6
Mean length5.9677687
Min length3

Characters and Unicode

Total characters35920
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCNG
2nd rowDiesel
3rd rowPetrol
4th rowDiesel
5th rowDiesel

Common Values

ValueCountFrequency (%)
Diesel 3205
53.2%
Petrol 2746
45.6%
CNG 56
 
0.9%
LPG 10
 
0.2%
Electric 2
 
< 0.1%

Length

2025-09-26T12:31:09.696917image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-26T12:31:09.834978image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
diesel 3205
53.2%
petrol 2746
45.6%
cng 56
 
0.9%
lpg 10
 
0.2%
electric 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 9158
25.5%
l 5953
16.6%
i 3207
 
8.9%
D 3205
 
8.9%
s 3205
 
8.9%
P 2756
 
7.7%
t 2748
 
7.7%
r 2748
 
7.7%
o 2746
 
7.6%
G 66
 
0.2%
Other values (5) 128
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 29769
82.9%
Uppercase Letter 6151
 
17.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 9158
30.8%
l 5953
20.0%
i 3207
 
10.8%
s 3205
 
10.8%
t 2748
 
9.2%
r 2748
 
9.2%
o 2746
 
9.2%
c 4
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
D 3205
52.1%
P 2756
44.8%
G 66
 
1.1%
C 56
 
0.9%
N 56
 
0.9%
L 10
 
0.2%
E 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 35920
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 9158
25.5%
l 5953
16.6%
i 3207
 
8.9%
D 3205
 
8.9%
s 3205
 
8.9%
P 2756
 
7.7%
t 2748
 
7.7%
r 2748
 
7.7%
o 2746
 
7.6%
G 66
 
0.2%
Other values (5) 128
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 9158
25.5%
l 5953
16.6%
i 3207
 
8.9%
D 3205
 
8.9%
s 3205
 
8.9%
P 2756
 
7.7%
t 2748
 
7.7%
r 2748
 
7.7%
o 2746
 
7.6%
G 66
 
0.2%
Other values (5) 128
 
0.4%

Transmission
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
Manual
4299 
Automatic
1720 

Length

Max length9
Median length6
Mean length6.8572853
Min length6

Characters and Unicode

Total characters41274
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManual
2nd rowManual
3rd rowManual
4th rowManual
5th rowAutomatic

Common Values

ValueCountFrequency (%)
Manual 4299
71.4%
Automatic 1720
28.6%

Length

2025-09-26T12:31:09.955490image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-26T12:31:10.082689image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
manual 4299
71.4%
automatic 1720
28.6%

Most occurring characters

ValueCountFrequency (%)
a 10318
25.0%
u 6019
14.6%
M 4299
10.4%
n 4299
10.4%
l 4299
10.4%
t 3440
 
8.3%
A 1720
 
4.2%
o 1720
 
4.2%
m 1720
 
4.2%
i 1720
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35255
85.4%
Uppercase Letter 6019
 
14.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 10318
29.3%
u 6019
17.1%
n 4299
12.2%
l 4299
12.2%
t 3440
 
9.8%
o 1720
 
4.9%
m 1720
 
4.9%
i 1720
 
4.9%
c 1720
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
M 4299
71.4%
A 1720
28.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 41274
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 10318
25.0%
u 6019
14.6%
M 4299
10.4%
n 4299
10.4%
l 4299
10.4%
t 3440
 
8.3%
A 1720
 
4.2%
o 1720
 
4.2%
m 1720
 
4.2%
i 1720
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41274
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 10318
25.0%
u 6019
14.6%
M 4299
10.4%
n 4299
10.4%
l 4299
10.4%
t 3440
 
8.3%
A 1720
 
4.2%
o 1720
 
4.2%
m 1720
 
4.2%
i 1720
 
4.2%

Owner_Type
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
First
4929 
Second
968 
Third
 
113
Fourth & Above
 
9

Length

Max length14
Median length5
Mean length5.1742814
Min length5

Characters and Unicode

Total characters31144
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFirst
2nd rowFirst
3rd rowFirst
4th rowFirst
5th rowSecond

Common Values

ValueCountFrequency (%)
First 4929
81.9%
Second 968
 
16.1%
Third 113
 
1.9%
Fourth & Above 9
 
0.1%

Length

2025-09-26T12:31:10.188566image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-26T12:31:10.321275image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
first 4929
81.6%
second 968
 
16.0%
third 113
 
1.9%
fourth 9
 
0.1%
9
 
0.1%
above 9
 
0.1%

Most occurring characters

ValueCountFrequency (%)
r 5051
16.2%
i 5042
16.2%
F 4938
15.9%
t 4938
15.9%
s 4929
15.8%
d 1081
 
3.5%
o 986
 
3.2%
e 977
 
3.1%
n 968
 
3.1%
c 968
 
3.1%
Other values (9) 1266
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 25089
80.6%
Uppercase Letter 6028
 
19.4%
Space Separator 18
 
0.1%
Other Punctuation 9
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 5051
20.1%
i 5042
20.1%
t 4938
19.7%
s 4929
19.6%
d 1081
 
4.3%
o 986
 
3.9%
e 977
 
3.9%
n 968
 
3.9%
c 968
 
3.9%
h 122
 
0.5%
Other values (3) 27
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
F 4938
81.9%
S 968
 
16.1%
T 113
 
1.9%
A 9
 
0.1%
Space Separator
ValueCountFrequency (%)
18
100.0%
Other Punctuation
ValueCountFrequency (%)
& 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 31117
99.9%
Common 27
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 5051
16.2%
i 5042
16.2%
F 4938
15.9%
t 4938
15.9%
s 4929
15.8%
d 1081
 
3.5%
o 986
 
3.2%
e 977
 
3.1%
n 968
 
3.1%
c 968
 
3.1%
Other values (7) 1239
 
4.0%
Common
ValueCountFrequency (%)
18
66.7%
& 9
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31144
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 5051
16.2%
i 5042
16.2%
F 4938
15.9%
t 4938
15.9%
s 4929
15.8%
d 1081
 
3.5%
o 986
 
3.2%
e 977
 
3.1%
n 968
 
3.1%
c 968
 
3.1%
Other values (9) 1266
 
4.1%

Mileage
Text

Distinct442
Distinct (%)7.3%
Missing2
Missing (%)< 0.1%
Memory size47.2 KiB
2025-09-26T12:31:10.653038image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length11
Median length9
Mean length9.3993685
Min length8

Characters and Unicode

Total characters56556
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)1.2%

Sample

1st row26.6 km/kg
2nd row19.67 kmpl
3rd row18.2 kmpl
4th row20.77 kmpl
5th row15.2 kmpl
ValueCountFrequency (%)
kmpl 5951
49.5%
17.0 173
 
1.4%
18.9 172
 
1.4%
18.6 119
 
1.0%
20.36 88
 
0.7%
21.1 87
 
0.7%
17.8 85
 
0.7%
16.0 76
 
0.6%
12.8 72
 
0.6%
20.0 71
 
0.6%
Other values (422) 5140
42.7%
2025-09-26T12:31:11.414673image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
k 6083
10.8%
. 6017
10.6%
6017
10.6%
m 6017
10.6%
p 5951
10.5%
l 5951
10.5%
1 5285
9.3%
2 3272
 
5.8%
0 1860
 
3.3%
7 1658
 
2.9%
Other values (8) 8445
14.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24068
42.6%
Decimal Number 20388
36.0%
Other Punctuation 6083
 
10.8%
Space Separator 6017
 
10.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5285
25.9%
2 3272
16.0%
0 1860
 
9.1%
7 1658
 
8.1%
5 1532
 
7.5%
8 1493
 
7.3%
4 1387
 
6.8%
9 1366
 
6.7%
6 1318
 
6.5%
3 1217
 
6.0%
Lowercase Letter
ValueCountFrequency (%)
k 6083
25.3%
m 6017
25.0%
p 5951
24.7%
l 5951
24.7%
g 66
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 6017
98.9%
/ 66
 
1.1%
Space Separator
ValueCountFrequency (%)
6017
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 32488
57.4%
Latin 24068
42.6%

Most frequent character per script

Common
ValueCountFrequency (%)
. 6017
18.5%
6017
18.5%
1 5285
16.3%
2 3272
10.1%
0 1860
 
5.7%
7 1658
 
5.1%
5 1532
 
4.7%
8 1493
 
4.6%
4 1387
 
4.3%
9 1366
 
4.2%
Other values (3) 2601
8.0%
Latin
ValueCountFrequency (%)
k 6083
25.3%
m 6017
25.0%
p 5951
24.7%
l 5951
24.7%
g 66
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56556
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
k 6083
10.8%
. 6017
10.6%
6017
10.6%
m 6017
10.6%
p 5951
10.5%
l 5951
10.5%
1 5285
9.3%
2 3272
 
5.8%
0 1860
 
3.3%
7 1658
 
2.9%
Other values (8) 8445
14.9%

Engine
Text

Distinct146
Distinct (%)2.4%
Missing36
Missing (%)0.6%
Memory size47.2 KiB
2025-09-26T12:31:11.680984image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.9015544
Min length5

Characters and Unicode

Total characters41292
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)0.5%

Sample

1st row998 CC
2nd row1582 CC
3rd row1199 CC
4th row1248 CC
5th row1968 CC
ValueCountFrequency (%)
cc 5983
50.0%
1197 606
 
5.1%
1248 512
 
4.3%
1498 304
 
2.5%
998 259
 
2.2%
2179 240
 
2.0%
1497 229
 
1.9%
1198 227
 
1.9%
1968 216
 
1.8%
1995 183
 
1.5%
Other values (137) 3207
26.8%
2025-09-26T12:31:12.081779image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 11966
29.0%
1 6169
14.9%
5983
14.5%
9 5477
13.3%
8 2498
 
6.0%
4 2111
 
5.1%
2 2055
 
5.0%
7 1714
 
4.2%
6 1212
 
2.9%
3 943
 
2.3%
Other values (2) 1164
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23343
56.5%
Uppercase Letter 11966
29.0%
Space Separator 5983
 
14.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6169
26.4%
9 5477
23.5%
8 2498
10.7%
4 2111
 
9.0%
2 2055
 
8.8%
7 1714
 
7.3%
6 1212
 
5.2%
3 943
 
4.0%
5 879
 
3.8%
0 285
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
C 11966
100.0%
Space Separator
ValueCountFrequency (%)
5983
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 29326
71.0%
Latin 11966
29.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6169
21.0%
5983
20.4%
9 5477
18.7%
8 2498
8.5%
4 2111
 
7.2%
2 2055
 
7.0%
7 1714
 
5.8%
6 1212
 
4.1%
3 943
 
3.2%
5 879
 
3.0%
Latin
ValueCountFrequency (%)
C 11966
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41292
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 11966
29.0%
1 6169
14.9%
5983
14.5%
9 5477
13.3%
8 2498
 
6.0%
4 2111
 
5.1%
2 2055
 
5.0%
7 1714
 
4.2%
6 1212
 
2.9%
3 943
 
2.3%
Other values (2) 1164
 
2.8%

Power
Text

Distinct372
Distinct (%)6.2%
Missing36
Missing (%)0.6%
Memory size47.2 KiB
2025-09-26T12:31:12.453830image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length11
Median length10
Mean length7.9326425
Min length6

Characters and Unicode

Total characters47461
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique69 ?
Unique (%)1.2%

Sample

1st row58.16 bhp
2nd row126.2 bhp
3rd row88.7 bhp
4th row88.76 bhp
5th row140.8 bhp
ValueCountFrequency (%)
bhp 5983
50.0%
74 235
 
2.0%
98.6 131
 
1.1%
73.9 125
 
1.0%
140 123
 
1.0%
78.9 111
 
0.9%
67.1 107
 
0.9%
null 107
 
0.9%
67.04 107
 
0.9%
82 101
 
0.8%
Other values (363) 4836
40.4%
2025-09-26T12:31:12.952377image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5983
12.6%
b 5983
12.6%
h 5983
12.6%
p 5983
12.6%
. 3693
7.8%
1 3663
7.7%
8 3195
6.7%
7 2316
 
4.9%
3 1771
 
3.7%
0 1721
 
3.6%
Other values (8) 7170
15.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19408
40.9%
Lowercase Letter 18377
38.7%
Space Separator 5983
 
12.6%
Other Punctuation 3693
 
7.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3663
18.9%
8 3195
16.5%
7 2316
11.9%
3 1771
9.1%
0 1721
8.9%
6 1696
8.7%
4 1435
 
7.4%
2 1365
 
7.0%
5 1266
 
6.5%
9 980
 
5.0%
Lowercase Letter
ValueCountFrequency (%)
b 5983
32.6%
h 5983
32.6%
p 5983
32.6%
l 214
 
1.2%
n 107
 
0.6%
u 107
 
0.6%
Space Separator
ValueCountFrequency (%)
5983
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3693
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 29084
61.3%
Latin 18377
38.7%

Most frequent character per script

Common
ValueCountFrequency (%)
5983
20.6%
. 3693
12.7%
1 3663
12.6%
8 3195
11.0%
7 2316
 
8.0%
3 1771
 
6.1%
0 1721
 
5.9%
6 1696
 
5.8%
4 1435
 
4.9%
2 1365
 
4.7%
Other values (2) 2246
 
7.7%
Latin
ValueCountFrequency (%)
b 5983
32.6%
h 5983
32.6%
p 5983
32.6%
l 214
 
1.2%
n 107
 
0.6%
u 107
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47461
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5983
12.6%
b 5983
12.6%
h 5983
12.6%
p 5983
12.6%
. 3693
7.8%
1 3663
7.7%
8 3195
6.7%
7 2316
 
4.9%
3 1771
 
3.7%
0 1721
 
3.6%
Other values (8) 7170
15.1%

Seats
Real number (ℝ)

Distinct9
Distinct (%)0.2%
Missing42
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean5.2787352
Minimum0
Maximum10
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size47.2 KiB
2025-09-26T12:31:13.109914image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q15
median5
Q35
95-th percentile7
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.80883955
Coefficient of variation (CV)0.15322602
Kurtosis4.5333633
Mean5.2787352
Median Absolute Deviation (MAD)0
Skewness1.8357921
Sum31551
Variance0.65422143
MonotonicityNot monotonic
2025-09-26T12:31:13.216149image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 5014
83.3%
7 674
 
11.2%
8 134
 
2.2%
4 99
 
1.6%
6 31
 
0.5%
2 16
 
0.3%
10 5
 
0.1%
9 3
 
< 0.1%
0 1
 
< 0.1%
(Missing) 42
 
0.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
2 16
 
0.3%
4 99
 
1.6%
5 5014
83.3%
6 31
 
0.5%
7 674
 
11.2%
8 134
 
2.2%
9 3
 
< 0.1%
10 5
 
0.1%
ValueCountFrequency (%)
10 5
 
0.1%
9 3
 
< 0.1%
8 134
 
2.2%
7 674
 
11.2%
6 31
 
0.5%
5 5014
83.3%
4 99
 
1.6%
2 16
 
0.3%
0 1
 
< 0.1%

New_Price
Text

MISSING 

Distinct540
Distinct (%)65.5%
Missing5195
Missing (%)86.3%
Memory size47.2 KiB
2025-09-26T12:31:13.574103image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.4332524
Min length4

Characters and Unicode

Total characters7773
Distinct characters18
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique349 ?
Unique (%)42.4%

Sample

1st row8.61 Lakh
2nd row21 Lakh
3rd row10.65 Lakh
4th row32.01 Lakh
5th row47.87 Lakh
ValueCountFrequency (%)
lakh 807
49.0%
cr 17
 
1.0%
4.78 6
 
0.4%
63.71 6
 
0.4%
95.13 6
 
0.4%
44.28 5
 
0.3%
11.26 5
 
0.3%
4.98 5
 
0.3%
47.87 5
 
0.3%
11.67 5
 
0.3%
Other values (532) 781
47.4%
2025-09-26T12:31:14.049341image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
824
10.6%
. 810
10.4%
L 807
10.4%
a 807
10.4%
k 807
10.4%
h 807
10.4%
1 518
 
6.7%
4 318
 
4.1%
7 297
 
3.8%
5 294
 
3.8%
Other values (8) 1484
19.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2877
37.0%
Lowercase Letter 2438
31.4%
Space Separator 824
 
10.6%
Uppercase Letter 824
 
10.6%
Other Punctuation 810
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 518
18.0%
4 318
11.1%
7 297
10.3%
5 294
10.2%
6 283
9.8%
2 274
9.5%
8 270
9.4%
3 254
8.8%
9 227
7.9%
0 142
 
4.9%
Lowercase Letter
ValueCountFrequency (%)
a 807
33.1%
k 807
33.1%
h 807
33.1%
r 17
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
L 807
97.9%
C 17
 
2.1%
Space Separator
ValueCountFrequency (%)
824
100.0%
Other Punctuation
ValueCountFrequency (%)
. 810
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4511
58.0%
Latin 3262
42.0%

Most frequent character per script

Common
ValueCountFrequency (%)
824
18.3%
. 810
18.0%
1 518
11.5%
4 318
 
7.0%
7 297
 
6.6%
5 294
 
6.5%
6 283
 
6.3%
2 274
 
6.1%
8 270
 
6.0%
3 254
 
5.6%
Other values (2) 369
8.2%
Latin
ValueCountFrequency (%)
L 807
24.7%
a 807
24.7%
k 807
24.7%
h 807
24.7%
C 17
 
0.5%
r 17
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7773
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
824
10.6%
. 810
10.4%
L 807
10.4%
a 807
10.4%
k 807
10.4%
h 807
10.4%
1 518
 
6.7%
4 318
 
4.1%
7 297
 
3.8%
5 294
 
3.8%
Other values (8) 1484
19.1%

Price
Real number (ℝ)

HIGH CORRELATION 

Distinct1373
Distinct (%)22.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.4794684
Minimum0.44
Maximum160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.2 KiB
2025-09-26T12:31:14.231121image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.44
5-th percentile1.7
Q13.5
median5.64
Q39.95
95-th percentile32.446
Maximum160
Range159.56
Interquartile range (IQR)6.45

Descriptive statistics

Standard deviation11.187917
Coefficient of variation (CV)1.1802262
Kurtosis17.092202
Mean9.4794684
Median Absolute Deviation (MAD)2.62
Skewness3.335232
Sum57056.92
Variance125.16949
MonotonicityNot monotonic
2025-09-26T12:31:14.376436image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.5 88
 
1.5%
5.5 84
 
1.4%
3.5 82
 
1.4%
4.25 73
 
1.2%
3.25 71
 
1.2%
3 68
 
1.1%
6.5 64
 
1.1%
2.5 63
 
1.0%
4 56
 
0.9%
4.75 53
 
0.9%
Other values (1363) 5317
88.3%
ValueCountFrequency (%)
0.44 1
 
< 0.1%
0.45 3
< 0.1%
0.5 2
< 0.1%
0.51 1
 
< 0.1%
0.53 2
< 0.1%
0.55 3
< 0.1%
0.6 2
< 0.1%
0.63 1
 
< 0.1%
0.65 2
< 0.1%
0.69 1
 
< 0.1%
ValueCountFrequency (%)
160 1
< 0.1%
120 1
< 0.1%
100 1
< 0.1%
97.07 1
< 0.1%
93.67 1
< 0.1%
93 1
< 0.1%
90 1
< 0.1%
85 1
< 0.1%
83.96 1
< 0.1%
79 2
< 0.1%

Interactions

2025-09-26T12:31:06.225054image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2025-09-26T12:31:05.798441image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-09-26T12:31:06.620550image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-09-26T12:31:03.808160image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-09-26T12:31:04.424563image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-09-26T12:31:05.231851image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-09-26T12:31:05.939075image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-09-26T12:31:06.789458image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-09-26T12:31:03.927705image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-09-26T12:31:04.537966image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-09-26T12:31:05.418033image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-09-26T12:31:06.095248image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2025-09-26T12:31:14.513077image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Unnamed: 0YearKilometers_DrivenSeatsPriceLocationFuel_TypeTransmissionOwner_Type
Unnamed: 01.0000.009-0.003-0.011-0.0070.0000.0040.0230.000
Year0.0091.000-0.5530.0350.4910.1390.0990.0960.250
Kilometers_Driven-0.003-0.5531.0000.196-0.2150.0090.0000.0130.000
Seats-0.0110.0350.1961.0000.2210.0200.1580.1470.035
Price-0.0070.491-0.2150.2211.0000.0740.1480.5950.058
Location0.0000.1390.0090.0200.0741.0000.0950.1840.161
Fuel_Type0.0040.0990.0000.1580.1480.0951.0000.1520.016
Transmission0.0230.0960.0130.1470.5950.1840.1521.0000.013
Owner_Type0.0000.2500.0000.0350.0580.1610.0160.0131.000

Missing values

2025-09-26T12:31:06.980865image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-26T12:31:07.238207image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-09-26T12:31:07.434194image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0NameLocationYearKilometers_DrivenFuel_TypeTransmissionOwner_TypeMileageEnginePowerSeatsNew_PricePrice
00Maruti Wagon R LXI CNGMumbai201072000CNGManualFirst26.6 km/kg998 CC58.16 bhp5.0NaN1.8
11Hyundai Creta 1.6 CRDi SX OptionPune201541000DieselManualFirst19.67 kmpl1582 CC126.2 bhp5.0NaN12.5
22Honda Jazz VChennai201146000PetrolManualFirst18.2 kmpl1199 CC88.7 bhp5.08.61 Lakh4.5
33Maruti Ertiga VDIChennai201287000DieselManualFirst20.77 kmpl1248 CC88.76 bhp7.0NaN6.0
44Audi A4 New 2.0 TDI MultitronicCoimbatore201340670DieselAutomaticSecond15.2 kmpl1968 CC140.8 bhp5.0NaN17.7
55Hyundai EON LPG Era Plus OptionHyderabad201275000LPGManualFirst21.1 km/kg814 CC55.2 bhp5.0NaN2.4
66Nissan Micra Diesel XVJaipur201386999DieselManualFirst23.08 kmpl1461 CC63.1 bhp5.0NaN3.5
77Toyota Innova Crysta 2.8 GX AT 8SMumbai201636000DieselAutomaticFirst11.36 kmpl2755 CC171.5 bhp8.021 Lakh17.5
88Volkswagen Vento Diesel ComfortlinePune201364430DieselManualFirst20.54 kmpl1598 CC103.6 bhp5.0NaN5.2
99Tata Indica Vista Quadrajet LSChennai201265932DieselManualSecond22.3 kmpl1248 CC74 bhp5.0NaN1.9
Unnamed: 0NameLocationYearKilometers_DrivenFuel_TypeTransmissionOwner_TypeMileageEnginePowerSeatsNew_PricePrice
60096009Toyota Camry HybridMumbai201533500PetrolAutomaticFirst19.16 kmpl2494 CC158.2 bhp5.0NaN19.8
60106010Honda Brio 1.2 VX MTDelhi201333746PetrolManualFirst18.5 kmpl1198 CC86.8 bhp5.06.63 Lakh3.2
60116011Skoda Superb 3.6 V6 FSIHyderabad200953000PetrolAutomaticFirst0.0 kmpl3597 CC262.6 bhp5.0NaN4.8
60126012Toyota Innova 2.5 V Diesel 7-seaterCoimbatore201145004DieselManualFirst12.8 kmpl2494 CC102 bhp7.0NaN9.5
60136013Honda Amaze VX i-DTECCoimbatore201570602DieselManualFirst25.8 kmpl1498 CC98.6 bhp5.0NaN4.8
60146014Maruti Swift VDIDelhi201427365DieselManualFirst28.4 kmpl1248 CC74 bhp5.07.88 Lakh4.8
60156015Hyundai Xcent 1.1 CRDi SJaipur2015100000DieselManualFirst24.4 kmpl1120 CC71 bhp5.0NaN4.0
60166016Mahindra Xylo D4 BSIVJaipur201255000DieselManualSecond14.0 kmpl2498 CC112 bhp8.0NaN2.9
60176017Maruti Wagon R VXIKolkata201346000PetrolManualFirst18.9 kmpl998 CC67.1 bhp5.0NaN2.6
60186018Chevrolet Beat DieselHyderabad201147000DieselManualFirst25.44 kmpl936 CC57.6 bhp5.0NaN2.5